# Generalization Bounds for Set-to-Set Matching with Negative Sampling

arXiv: https://arxiv.org/abs/2302.12991 · HTML: https://mlsanity.com/paper/2302.12991/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 25 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding](https://mlsanity.com/paper/1707.09173/) (2017)
- [Why Mixup Improves the Model Performance](https://mlsanity.com/paper/2006.06231/) (2020)

## Built on by (most cited first, 1 of 1)

- [On permutation-invariant neural networks](https://mlsanity.com/paper/2403.17410/) (2024)

## Abstract (arXiv)

The problem of matching two sets of multiple elements, namely set-to-set matching, has received a great deal of attention in recent years. In particular, it has been reported that good experimental results can be obtained by preparing a neural network as a matching function, especially in complex cases where, for example, each element of the set is an image. However, theoretical analysis of set-to-set matching with such black-box functions is lacking. This paper aims to perform a generalization error analysis in set-to-set matching to reveal the behavior of the model in that task.